中文摘要：

针对自主水下机器人对广义行为环境自适应能力差的问题,给出基于平方根无色卡尔曼滤波的广义行为环境建模方法。在广义行为环境的离线参考模型中,有一些参数是时变的、是无法事先预知的,必须通过传感器探测的信息进行实时估计和预测。采用平方根无色卡尔曼滤波算法,根据在线传感器信息以及离线参考模型,实时地估计出广义行为环境的状态和参数。主要研究自主水下机器人自身行为环境建模,以远程水下自主机器人的推进系统为例,构建一种推进器效率损失因子的故障模型结构,应用平方根无色卡尔曼滤波对水下自主机器人的状态和推进器故障参数进行在线联合估计。利用远程自主水下机器人的数学模型进行仿真验证,试验结果表明了算法的有效性,并对影响平方根无色卡尔曼滤波算法估计性能的因素进行了分析。

英文摘要：

Due to the poor adaptive capacity of long-range autonomous underwater vehicles（AUV） for general behavior of the environment,a square-root unscented kalman filter（SUKF） based modeling is proposed.In the offline model of general behavior of the environment,some parameters are time-varying,or not possible to be predicted,these parameters must be estimated and predicted through the information detected by the sensors.According to the sensor＇s information online and model offline,SUKF is used to estimate the state and parameter about the general behavior of the environment in real time.Modeling for behavior environment of AUV is the main work,taking propulsion system as an example,a model structure of fault used actuator effectiveness factors（AEFs） is constructed,then the SUKF is used for on-line joint estimation of both the states and AEFs parameters of AUV.Simulators are conducted by using the model of long-range AUV,and the results show the validity of the algorithm.Finally,the factors that affect the performance for estimation of SUKF are analyzed.